Auxiliary Reward

Auxiliary rewards in reinforcement learning aim to improve the efficiency and robustness of training agents by supplementing the primary reward signal with additional, often heuristically designed, feedback. Current research focuses on developing methods to generate these auxiliary rewards automatically, using techniques like transition distance representation learning and vision-language models, while mitigating the negative impact of noise and misspecification in these auxiliary signals. This work is crucial for addressing the challenges of sparse rewards and human bias in designing reward functions, ultimately leading to more reliable and efficient reinforcement learning agents for complex real-world applications.